However, now objects are linked to different types of neighbors, if all neighbors are treated as the same, it may cause information loss problems.

for example, the entity "重庆 (Chongqing)" is a very important aspect characterizing the politician "薄熙来(Bo Xilai)"  figure 4: Example of Morph-Related Heterogeneous Information Network since he governed it, and if a morph m which is also highly correlated with "重庆 (Chongqing)", it is very likely that "Bo Xilai" is the real target of m. the semantic features generated from neighbors such as the entity "重庆 (Chongqing)" should be treated differently from other types of neighbors such as "人才(talented people)" .


For example, the tweet from Twitter "...把 薄 熙 来 称 作"平 西 王"或 者"不 厚"... (...call Bo Xilai"peace west king" or "buhou"...)" contains both the morph and the real target 薄熙 来 (Bo Xilai).

we propose to integrate information from another source (e.g.


Because of such social correlation, close social neighbors in social media such as Twitter and Weibo may post similar information, or share similar opinion.

we can utilize social correlation to assist in resolving morphs.


The poor performance based on surface features shows that morph resolution task is very challenging since 70% of morphs are not orthographically similar to their real targets.

capturing a morph's semantic meaning is crucial.


As shown in Table9 (K is the number of predefined topics), PLSA is not quite effective mainly because traditional topic modeling approaches do not perform well on short texts from social media.

in this paper we choose a simple method based on temporal distribution to detect target candidates.


We use structured logistic normal distributions to represent contextual effects.

the simplest is the vanilla (V) context model, • For each frame k, draw global parameters from diffuse priors: prevalence α k and variability the vector η * ,s,r,t encodes the relative logodds of the different frames for events appearing in the context (s, r, t).


The relative margin is the ratio of the absolute, or maximum margin, to the spread of the projected data.

the RMM learns a large margin solution relative to the spread of the data, or in other words, creates a max margin while simultaneously bounding the spread of the projected data.


During translation, these PAS ambiguities will greatly affect the PAS-based translation models.

in order to incorporate the bilingual PAS into machine translation effectively, we need to decide which target-side-like PAS should be chosen for a specific source-side PAS.


The targetside-like PAS is selected only according to the language model and translation probabilities, without considering any context information of PAS.

it would be difficult for the decoder to distinguish the source-side PAS from different context.


This results in a bad translation because the gap string "对(dui) 运动员 (yun-dong-yuan)" and predicate "是(shi)" should be translated separately, just as Figure 4(a) shows.

we can see that the attachment decision in (Zhai et al., 2012) is sometimes unreasonable and the IC also cannot be used for PAS disambiguation at all.


These statistics demonstrate the importance of handling the role ambiguity and gap ambiguity in the PAS-based translation framework.

we believe that our PAS disambiguation method would be helpful for translation.


Consequently, since PASTR does not consider the inside context during translation, the Gap PASs, which account for 47% (1539/3241) of all matching PASs, might be handled inappropriately in the PAS-based translation framework.

integrating the inside context into PASTRs, i.e., using the proposed IC-PASTRs, would be helpful for translation.


This indicates that comparing with PASTR, IC-PASTR is more capable of distinguishing different PASs.

based on this advantage, although the number of matching PASs decreases, IC-PASTR still improves the translation system using PASTR significantly.


In the example, the automatic SRL system ignores the long preposition phrase "对 经济复苏 、尤其是 恢复 投资信 心" for the PAS.

the system using PASTRs can only attach the long phrase to the predicate "是" according to the parse tree, and meanwhile, make use of a transformation rule as follows: source-side PAS(是) target-side-like PAS In this way, the translation result is very bad, just as Figure 5(b) shows.


However, the difference in the richness cannot be transferred into English because English has almost no inflectional case system.

one cannot determine the mother tongue of a given nonnative text from the inflectional case.


The vector is constructed based on the relative frequencies of trigrams.

the distance is naturally defined by the Euclidean distance between two vectors.


Besides, there are a large number of language pairs to compare.

we need an efficient and effective way to analyze the experimental results.


The quantity (r tk − r ti ) 2 is directly related to the decrease in the distance between D i and D k and similarly, (r tj − r ti ) 2 to that between D i and D j in the vectorbased method.

the greater s is, the higher the chance that the cluster tree changes.


In contrast, noun-noun compounds or similar constructions are more common in Russian and Swedish.

nn of nn becomes relatively frequent in the Italic Englishes.


The vast majority of contemporary research that investigates statistical properties of language deals with characterizing words by extracting information about their behavior from large corpora.

co-occurrence of words in n-word windows, syntactic structures, sentences, paragraphs, and even whole documents is captured in vector-space models built from text corpora (Turney and Pantel, 2010;Basili and Pennacchiotti, 2010;Erk and Padó, 2008;Mitchell and Lapata, 2008;Bullinaria and Levy, 2007;Jones and Mewhort, 2007;Pado and Lapata, 2007;Lin, 1998;Landauer and Dumais, 1997;Lund and Burgess, 1996;Salton et al., 1975).


To conclude the illustration, we observe that there are some broad similarities between the different copora in terms of the distribution of pairs of word types.

texts seem to be mainly made of pairs of weakly associated words -about half the pairs of word types lie between PMI of 0.5 and 1.5, in all the examined collections (52% for essays, 44% for each of WSJ and young reader corpora).


First, we observe that, perhaps contrary to expectation, the proportion of the highest values of PMI (the area to the right of PMI=4 in Figure 2) does not yield a consistent correlation with essay scores.

inasmuch as highest PMI values tend to capture multi-word expressions (South and Africa; Merill and Lynch), morphological variants (bids and bidding), or synonyms (mergers and takeovers), their proportion in word type pairs does not seem to give a clear signal regarding the quality of writing.


Such a model renders inference and learning infeasible.

we propose a clearer model by a directed graph, as illustrated in Figure 1 (where nodes are represented by replacements r instead of the variables X r , for readability).


However, many of the most important features are lexically dependent on the predicate and cannot been generalized.

specific annotations are required for each new predicate to be analyzed.


For instance, in example (1), all the argument positions of the second occurrence of the predicate loss are missing, but they can be easily inferred from the previous instance of the same predicate.

( 1) we propose to exploit this property in order to capture correctly how the fillers of all predicate arguments evolve through a document.


Our algorithm, ImpAr, processes the documents sentence by sentence, assuming that sequences of the same predicate (in its nominal or verbal form) share the same argument fillers (explicit or implicit) 3 .

for every core argument arg n of a predicate, ImpAr stores its previous known filler as a default value.


Therefore [Quest Medical Inc] becomes the default arg 0 of plan.

in the following sentence the damping factor is: 0.5 = 0.5 1 its salience score changes to 260 − 100+100•0.5 = 210.


For each missing argument, the gold-standard includes the whole coreference chain of the filler.

the scorer selects from all coreferent mentions the highest Dice value.


Section 4.1), which showed that k = 7 retrieves about 92% of the related lemmas retrieved for k = 20 with a much smaller number of true negatives.

the evaluation on the R-sample might overestimate the recall, but only slightly so, while the P-sample yields a reliable estimate of precision by reducing the number of true negatives in the sample.


We denote D π = {(a, s, l) : l = } ⊆ L π as the set of those arguments originally linked to the corresponding Wikipedia page (like the top three linked arguments in Table 1).

in the rest of this section we will focus only on the remaining triples (a, s, ) ∈ U π , where e., those triples whose arguments are not semantically annotated.


Although efforts towards the taxonomization of Wikipedia categories do exist in the literature (Ponzetto and Strube, 2011; Nastase and Strube, 2013), the results are of a lower quality than a hand-built lexical resource.

as was done in previous work (Mihalcea and Moldovan, 2001;Ciaramita and Altun, 2006;Izquierdo et al., 2009;Erk and McCarthy, 2009;Huang and Riloff, 2010), we pick out our semantic classes C from WordNet and leverage its manually-curated taxonomy to associate our arguments with the most suitable class.


We next investigate whether simple paraphrasing could account for the above results; it may be the case that human summarizers simply replace words in the source text with synonyms, which can be detected with distributional similarity.

we merged similar caseframes into clusters according to the distributional semantic similarity defined in Section 3.1, and then repeated the previous experiment.


In the end, for each entity mentioned in the document we have a unique identifier, a list with all its mentions in the document and a list of class labels from Freebase.

of this process, we obtain for each sentence in the corpus a representation as exemplified in Figure 1 (1).


At this point, we encounter a user barge-in correcting the desired choice.

the dialogue manager needs to update its initial hypotheses and communicate this to NLG.


In mixture models, a phrase pair's distribu-tion across subcorpora is captured only implicitly, by probabilities that reflect the prevalence of the pair within each subcorpus.

vSM adaptation occurs at a much finer granularity than mixture model adaptation.


One can think of several other ways of defining the vector space that might yield even better results than those reported here.

vSM adaptation is not limited to the variant of it that we tested in our experiments.


To map the BC score onto a range from 0 to 1, we first normalize each weight in the vector by dividing it by the sum of the weights.

we get the probability distribution of a phrase pair or the phrase pairs in the dev data across all subcorpora: To further improve the similarity score, we apply absolute discounting smoothing when calculating the probability distributions p i (f, e).


In general many program input formats are nested tree structures, in which the tree root denotes the entire chunk of program input data and each chunk (tree node) can be further divided into sub-chunks or primitive fields that appear in the program input (see Figure 3).

our objective is to predict a dependency tree that correctly represents the structure of the program input.


However, as is well known, HBMs are "ideal" learning systems, assuming access to unlimited computational resources that may not be available to child language learners.

it remains crucial to carefully assess the use of HBMs along with alternative, possibly simpler, candidate models.


directional similarity  and meta-classification over similarity values (Berant et al., 2011).

several knowledge resources of inference rules were released, containing the top scoring rules for each predicate (Schoenmackers et al., 2010;Berant et al., 2011;Nakashole et al., 2012).


We hypothesize that a specific rank ordering may be attributed to small differences in the multinomial probabilities, which can lower rank-based similarities when one of the compared orderings is perturbed due to slightly different probability values.

we consider the top-k senses as an unordered set, with equal importance in the signature.


The STS task uses human similarity judgments on an ordinal scale from 0 to 5.

in ADW we adopted a similar approach to generating similarity values to that adopted by other participating systems, whereby a supervised system is trained to combine multiple similarity judgments to produce a final rating consistent with the human annotators.


This is supported by our analysis of the agreement by POS (see Table 1): the agreement on nouns and adjectives lies between the two agreement scores previously reported on nouns.


The verb senses are very fine-grained and thus present a difficult alignment task.

we had an expert annotator correct the verbal part of the gold standard set.


Such granularity differences also led to the addition of proto-frames in SALSA 2 (Rehbein et al., 2012).

manual correction or extension of a multilingual FrameNet based on FNWKde may be desired for specific applications.


For most researchers, this is prohibitively expensive.

web-mined parallel text has become the exclusive purview of large companies with the computational resources to crawl, store, and process the entire Web.


Given the N -best compressions from the decoder, we evaluate the yield of the trimmed trees using a language model trained on the Gigaword (Graff, 2003) corpus and return the compression with the highest probability.

the decoder is quite flexible -its learned scoring function allows us to incorporate features salient for sentence compression while its language model guarantees the linguistic quality of the compressed string.


A: but since LCDs seems to be uh a definite yes, C: We're having pushbuttons on the outside C: and then on the inside an LCD with spinning wheel, Meetings are a common way to collaborate, share information and exchange opinions.

automatically generated meeting summaries could be of great value to people and businesses alike by providing quick access to the essential content of past meetings.


We are not aware of any existing work generating abstractive summaries for conversations.

we compare our full system against a supervised utterance-level extractive method based on SVMs along with the baselines.


For the present research, we introduce a similar method that leverages the distributions of document-level features in the training corpus to incorporate a statistical document planning component.

we are able to create a streamlined statistical NLG architecture that balances natural human-like variability with appropriate and accurate information.


For example, since (的, of) is an entry in LLRlex and we have learned that "of" is much more likely to be behind the translation of the succeeding word.


Compared with their work, our focus is to induce phrase pairs directly from monolingual data rather than comparable data.

finding the candidate parallel sentences is not possible in our situation.


different arguments, prepositions, parts of speech, etc.).

we define an additional type level feature to be the ratio of the number of new domain n-grams (up to length three) that contain word w and which do not appear in the old domain to the total number of new domain n-grams containing w. With N w indicating the set of n-grams in the new domain which contain w, O w indicating the set of n-grams in the old domain which contain w, and |N w − O w | indicating the n-grams which contain w and appear in the new but not the old domain, we define Type:Contextas |Nw−Ow| |Nw| .


For microaveraging, we compute a separate confusion matrix for each word type on the French side, compute P/R/F for each of these separately, and then average the results.

(micro-F is not a function of micro-P and micro-R.) The AUC and macro-averaged scores give a sense of how well the system is doing on a type-level basis (essentially weighted by type frequency), while the micro-averaged scores give a sense as to how well the system is doing on individual types, not taking into account their frequencies.


While retrieving patterns, we also need to enforce that a pattern be not completely filled just by adding the target words t, as under these conditions there would be no room to achieve any kind of creative effect.

we also require that the patterns retrieved by CompatiblePatterns(•) have more empty slots than the size of t. The minimum and maximum number of excess slots in the pattern are two other meta-parameters controlled by Θ. CompatiblePatterns(•) returns compatible patterns ordered by their frequency, i.e.


More simply put, we count how many of the phonetic prefixes of the words in the sentence are repeated, and then we normalize this value by the total number of phonemes in s. The rhyme feature works exactly in the same way, with the only difference that we invert the phonetic representation of each word before adding it to the TRIE.

we give higher scores to sentences in which several words share the same phonetic ending.


Given the corrections at various positions in a sentence, the system can readily come up with the corrected sentence.

a natural way to encode the output space of grammatical error correction requires information about sentence position, error type (e.g., noun number error), and correction (e.g., cat).


However, there are exponentially many different combinations of corrections, and it is not possible to consider all combinations.

instead of solving the original inference problem, we will solve an approximate inference problem by introducing the following decomposable assumption: Measuring the output quality of multiple corrections can be decomposed into measuring the quality of the individual corrections.


That two Boolean variables Z 1 and Z 2 are mutually exclusive can be handled using a simple inequality Z 1 + Z 2 ≤ 1.

the following inequality correctly enforces article-noun agreement: where the article at p 1 modies the noun at p 2 .


Similarly, the weight w Art,1,ɛ of Z ɛ Art,1 (delete article A) is also likely to be small because of the missing article.

if one considers the two corrections separately, they are both unlikely to appear in the nal corrected output.


To keep symbols clear, let Z = {Z u |Z u = Z k l,p , ∀l, p, k} be the set of rst order variables, and where Z u and Z v are rst order variables: The denition of X u,v states that a second order variable is set to 1 if and only if its two component rst order variables are both set to 1.

it combines two corrections into a single correction.


It has been estimated that at least half of the world's stored knowledge, both printed and digital, has geographic relevance, and that geographic information pervades many more aspects of humanity than previously thought (Petras, 2004;Skupin and Esperbé, 2011).

there is value in connecting linguistic references to places (e.g.


However, argument extraction is much different from SRL in the sense that, while the relationship between a predicate and its arguments in SRL can be mainly decided from the syntactic structure, the relationship between an event trigger and its arguments are more semantics-based, especially in Chinese, as a paratactic (e.g., discourse-driven and pro-drop) language with the wide spread of ellipsis and the open flexible sentence structure.

some arguments of a specific event mention are far away from the trigger and how to recover those inter-sentence arguments becomes a challenging issue in Chinese argument extraction.


In a word, above two issues indicate that syntactic feature-based approaches are limited in identifying Chinese arguments and it will lead to low recall in argument identification.

employing those high level information to capture the semantic relation, not only the syntactic structure, between the trigger and its long distance arguments is the key to improve the performance of the Chinese argument identification.


For the errors in the syntactic parsing, the second single-morpheme trigger is often assigned a wrong tag (e.g., NN, JJ) and this leads to the errors in the argument extraction.

inferring the arguments of the second singlemorpheme trigger from that of the first one based on Parallel relation is also an available way to recover arguments.


We cannot expect that the phrases we extract in text will be exact matches of the typed relational patterns learned by PATTY.

for better recall, we must accept fuzzy matches.


All confidence weights are normalized to values The limitation of the uniform weight approach is that each pattern is considered equally good for suggesting candidate types.

this approach does not take into account the intuition that an entity occurring with a pattern which is highly specific to a given type is a stronger signal that the entity is of the type suggested.


Suppose that we now encounter a new entity pair (x, y) occurring with a phrase that matches p. We can compute the likelihood of x and y being of types t 1 and t 2 , respectively, from the likelihood of p cooccurring with entities of types t 1 , t 2 .

we are interested in the type-pattern likelihood, defined as follows: Definition 6 (Type-Pattern Likelihood) The likelihood of p co-occurring with an entity pair (x, y) of the types (t 1 , t 2 ) is given by: (1) where t 1 and t 2 are the types of the arguments observed with p from a corpus such as Wikipedia.


In the case of porting a system to new domains entity information will be unreliable or missing.

in our domain adaptation experiments on the ACE 2005 data (Section 6.3) we will not rely on this gold information but rather train a system using PET (target mentions only marked with E1/E2 and no gold entity label).


6 In all our experiments, we model argument order of the relations explicitly.

for instance for the 7 coarse ACE 2004 relations, we build 14 coarse-grained classifiers (two for each coarse ACE 2004 relation type except for PER-SOC, which is symmetric, and one classifier for the none relation).


However, in a realistic setting we do not have gold entity info available, especially not in the case when we apply the system to any kind of text.

in the domain adaptation setup we assume entity boundaries given but not their label.


In contrast, adding lexical information combined with syntax can help to improve performance: the syntactic structure enriched with lexical information provides a feature space where syntax constrains lexical similarity obtained from unlabeled data.

semantic syntactic tree kernels appear to be a suitable mechanism to adequately trade off the two kinds of information.


DSMs ignore derivational morphology altogether.

they cannot provide meaning representations for new derived forms, nor can they harness the systematic relation existing between stems and derivations (any English speaker can infer that to rebuild is to build again, whether they are familiar with the prefixed form or not) in order to mitigate derived-form sparseness problems.


We saw that morphologically derived words tend to appear less frequently than their stems, and in many cases the meanings are close.

we expect a stem-only "composition" method to be a strong baseline in the morphological setting.


The underlying assump-tion is that a vector, in order to be a good representation of the meaning of the corresponding word, should lie in a region of semantic space populated by intuitively similar meanings, e.g., we are more likely to have captured the meaning of car if the NN of its vector is the automobile vector rather than potato.

to measure the quality of a given vector, we can look at the average similarity score provided by humans when comparing this very vector with its own NNs.


However, the stemploitation hypothesis is dispelled by the observation that both models significantly outperform the stem baseline (p<.001), despite the fact that the latter, again, has good performance, significantly outperforming the corpusderived vectors (p < .001).

we confirm that compositional models provide higher quality vectors that are capturing the meaning of derived forms beyond the information provided by the stem.


For large α 0 ≫ k, the distribution is highly peaked around the mean (conversely, when α 0 ≪ k, the mean lies in a valley).

the Dirichlet parameters of a language cluster characterize both the average HMMs of individual languages within the cluster, as well as how much we expect the HMMs to vary from the mean.


In a sentence with 3 referents that each have 10 RE candidates and can be freely ordered, the number of surface realizations increases from 6 to 6•10 3 , assuming that the remaining words can not be syntactically varied.

even when the generation problem is restricted to these tasks, a fully integrated architecture faces scalability issues on realistic corpus data.


The only remarkable difference is that the accuracy of the individual components is, in each case, lower when they are applied as the second step in the pipeline.

the RE accuracy suffers from mistakes from the predicted syntax in the same way that the quality of syntax suffers from predicted REs.


Due to the structure of the algorithm the cardinality of all hypotheses in H s increases in each step.

only hypotheses of the same cardinality init sets H s , H t 3: for all e ∈ V e do 9:   are compared in the pruning step.


As soon as this condition is violated, all further extensions will also violate the condition.

these partial hypotheses can be left out.


We conduct experiments on letter based 1:1 substitution ciphers, the homophonic substitution cipher Zodiac-408, and word based 1:1 substitution ciphers.

given a set of symbols V eval with unigram counts N (v) for v ∈ V eval , and the total amount of running symbols the SER can be seen as a weighted form of the MER, emphasizing errors for frequent words.


This is due to the probability normalization which is done according to the nodes connectivity.

the transition probability between any two nodes i, j is defined as: For any non-connected pair of nodes, P ij =0.


For any random walk the number of steps taken to traverse between any two nodes is called the hitting time (Norris, 1997).

the hitting time between a noisy and a normalized pair of nodes (n, m) with a walk r is h r (n, m).


Which is the relative frequency of the average hitting of those two nodes, H(n, m), and all other normalized nodes linked to that noisy word.

l(n, m), is calculated as: Furthermore, we add another similarity cost between a noisy word and a normalized word based on the lexical similarity cost, SimCost(n, m), which we will describe in the next section.


First, unlike previous studies that use parallel text and bilingual features, such as (Rarrick et al., 2011), our method only requires monolingual text as input.

our method can be used in monolingual Web data mining where bilingual information is unavailable.


Such a gappy-phrase is difficult for SMT systems to correctly generate and causes a phrase salad.

we define a feature to evaluate how likely a sentence contains gappy-phrases in a complete form without missing sub-phrases.


Sequential Pattern Mining It is costly to manually collect a lot of such gappy-phrases.

we regard the task as sequential pattern mining and apply PrefixSpan proposed by Pei et al.


We also obtain about 74K and 42K phrases from humangenerated and machine-translated sentences in the English dataset (21K of them are common).

phrase Selection of sequential pattern mining, we can gather a huge number of gappy-phrases from human-generated and machine-translated text, but as we described above, many of them are common.


in the early period after after the known as to and also and Human more than MT and but the not only but also no not with as well as not not The gappy-phrases depend on each other, and the more phrases extracted from human-generated (machine-translated) text are found in a sentence, the more likely the sentence is human-generated (machine-translated).

we compute the feature as where w i is a weight of the i-th phrase, and δ(i, s) is a Kronecker's delta function that takes 1 if the sentence s includes the i-th phrase and takes 0 otherwise.


Because English is a configurational language, function words are less flexible than case markers in Japanese.

sMT systems may better handle English function words, which potentially decreases the effect of FW LMs in our method.


As Lopez (2008) describes, a phrase-salad is a common phenomenon that characterizes current SMT results.

we expect that our method is basically effective on different language pairs.


When executed, these queries return all entities e that satisfy the given relationship.

our task is to find the query z that best captures the semantics of the question x.


However, most problem reports and corresponding aid messages were not successfully exchanged between victims and local governments or humanitarian organizations, overwhelmed by the vast amount of information.

victims could not receive necessary aid and humanitarian organizations wasted resources on redundant efforts.


To the best of our knowledge, this is the first work that aims at evaluating directly the joint information of the sentiment and aspect assignment at the sub-sentential level of full reviews; most existing studies either focus on indirect evaluation of the produced models (e.g., classifying the overall sentiment of sentences (Titov and McDonald, 2008a;Brody and Elhadad, 2010) or even reviews (Nakagawa et al., 2010; Jo and Oh, 2011)) or evaluated solely at the sentential or even document level.

in order to evaluate our methods, we created a new dataset which will be publicly released.


Our labels encoding aspect and sentiment level can be regarded as clusters.

we can apply techniques developed in the context of clustering evaluation.


Finally, examples 6-8 are too short and not informative at all which indicates that the segmentation tool does not always predict a desirable segmentation.

automatic induction of segmentation may be a better option.


By applying simple heuristics such as treating all verbs or verb phrases as opinion candidates would not help because it would introduce a large number of negative candidates and lower the accuracy of relation extraction (only 52% of the opinion expressions are verbs or verb phrases and 64% of the opinion targets are noun or noun phrases in the corpus we used).

a more effective candidate generation method is needed to allow more candidates while limiting the number of negative candidates.


But many other edits occur even in NPOV disputes, including edits to fix spelling or grammatical errors, simplify the language, make the meaning more precise, or even vandalism (Max and Wisniewski, 2010).

in order to extract a high-precision set of bias-driven edits, we took advantage of the comments that editors can associate with a revision-typically short and brief sentences describing the reason behind the revision.


Unlike the blogs, online debates and opinion pieces which have been the major focus of previous work, bias in reference works is undesirable.

the expression of bias is more implicit, making it harder to detect by both computers and humans.


This included similarity measures introduced by Wu and Palmer (1994) (wup), Leacock and Chodorow (1998) (lch), Resnik (1995) (res), Jiang and Conrath (1997) (jcn), Lin (1998) (lin), Banerjee and Pedersen (2003) (lesk), Hirst and St-Onge (1998) (hso) and Patwardhan and Pedersen (2006) (vector and vpairs) respectively.

settings and properties were changed systematically and shared with Pedersen who attempted to produce the new results with his own implementations.


Due to pressure to publish or other time limitations, they cannot be carried out for each evaluation.

it is important to share our experiments, so that other researchers (or students) can take this up.


55-58) with line numbers Topical segmentations of this poem are difficult to produce because there is still some structural form (i.e., punctuation) which may dictate where a boundary lies, but the imagery, places, times, and subjects of the poem appear to twist and wind like a vision in a dream.

placing a topical boundary in this text is a highly subjective task.


On the other hand high or comparable IAA for random queries would mean that annotations have strong biases.

we have the following four pairs of annotation experiments: flat and nested segmentation of queries from crowdsourcing, corresponding flat and nested gold annotations, flat and nested segmentation of English sentences from crowdsourcing, and flat and nested segmentations for randomly generated queries through crowdsourcing.


Inter Annotator Agreement is the only way to judge the reliability of annotated data in absence of an end application.

before we can venture into analysis of the experimental data, we need to formalize the notion of IAA for flat and nested queries.


For instance, we observed that a total of 128 turkers have provided the flat annotations for Q700, when we had only asked for 10 annotations per query.

on average, a turker has annotated only 7.81% of the 700 queries.


Let us denote this expected cumulative probability distribution for flat segmentation as P d (x; |q|) = the probability that for a pair of randomly chosen flat segmentations of q, q m and q n , d(q m , q n ) ≥ x.

likewise, let P d 1 (x; |q|) and P d 2 (x; |q|) be the respective probabilities that for any two nested segmentations q m and q n of q, the following holds: We define the IAA under random bias model as (k is 1, 2 or null): S is the expected probability of observing a similar or worse agreement by random chance, averaged over all pairs of annotations for all queries, and not a chance corrected IAA metric such as α.


Likewise, let P d 1 (x; |q|) and P d 2 (x; |q|) be the respective probabilities that for any two nested segmentations q m and q n of q, the following holds: We define the IAA under random bias model as (k is 1, 2 or null): Thus, S is the expected probability of observing a similar or worse agreement by random chance, averaged over all pairs of annotations for all queries, and not a chance corrected IAA metric such as α.

s = 1 implies that the observed agreement is almost always better than that by random chance and s = 0.5 and 0 respectively imply that the observed agreement is as good as and almost always worse than that by random chance.


The observed heights are much lower than the expected values clearly implying the preference of the annotators for more balanced trees.

the crowd seems to choose the middle path, avoiding extremes and hence may not be a reliable source of annotation for query segmentation.


If the acquired question-answer corpus contains many deceptive answers, it would be meaningless to perform further knowledge mining tasks.

as the first step, we need to predict and filter out the deceptive answers.


For example, if you ask the weather in mountain view, someone may just post the link to "http://www.weather.com/".

besides the existence of URL, we also use the following URL features: 1).


From Equation 1, when iteratively computing the  Table 3: Results With User Preference Graph final scores, the authority scores for these malicious users will be accumulated to large values.

it is hard to distinguish whether the high authority score represents real expert or malicious user.


Due to the variety of word choices and inherent ambiguities in natural languages, bag-ofwords approaches with simple surface-form word matching tend to produce brittle results with poor prediction accuracy (Bilotti et al., 2007).

researchers put more emphasis on exploiting both the syntactic and semantic structure in questions/sentences.


For instance, when a word refers to a named entity, the particular sense and meaning is often not encoded.

relations such as "Apple" is-a "company" and "Jaguar" is-a "car" cannot be found in WordNet.


However, one by-product of this model is the word embedding learned in its hidden-layer, which can be viewed as capturing the word meaning in some latent, conceptual space.

vectors of related words tend to be close to each other.


If we have known some words to be opinion words, the words which those opinion words modify will have high probability to be opinion targets.

identifying the aforementioned opinion relations between words is important for extracting opinion targets from reviews.


When the data is insufficient, it would suffer from data sparseness and may make the performance decline.

from the above analysis, we can observe that the size of the corpus has impacts on these two kinds of methods, which arises some important questions: how can we make selection between syntax based methods and alignment based method for opinion target extraction when given a certain amount of reviews?


It's not only to extract a lexicon or list of opinion targets, but also to find out each opinion target mentions in reviews.

the contextual words are usually selected as the features to indicate opinion targets in sentences.


If we directly use the alignment model, a noun (noun phrase) may align with other unrelated words, like prepositions or conjunctions and so on.

we set constrains on the model: 1) Alignment links must be assigned among nouns/noun phrases, adjectives/verbs and null words.


Concretely, we create a Sentiment Graph to model opinion relations among opinion word/target/pattern candidates and apply random walking to estimate confidence of them.

confidence of pattern is considered in a unified process.


However, the big challenge for fully supervised method is the lack of annotated training data.


Hence their confidence will be low although they may be extracted by some high quality patterns.

the first stage is incapable of dealing with the long-tail opinion target problem.


Fortunately, a large proportion of general noun noises are the most frequent words in common texts.

we can generate a small domain-independent general noun (GN) corpus from large web corpora to cover some most frequently used general noun examples.


We believe it is because Ours-Stage1 estimated confidence of patterns so false opinion relations are reduced.

the consideration of pattern confidence is beneficial as expected, which alleviates the false opinion relation problem.


At the same time, Ours-Full has much better performance than Ours-GN-Only which indicates that Ours-Full can filter out more noises other than the initial negative examples.

our self-learning strategy alleviates the shortcoming of false opinion target problem.


As one example, the predicate "cure", which has a positive connotation typically takes arguments with negative connotation, e.g., "disease", when used as the "relieve" sense.

3 in order to attain a broad coverage lexicon while maintaining good precision, we guide the induction algorithm with multiple, carefully selected linguistic insights: [1] distributional similarity, [2] semantic parallelism of coordination, [3] selectional preference, and [4] semantic prosody (e.g., Sinclair (1991), Louw (1993), Stubbs (1995), Stefanowitsch and Gries (2003))), and also exploit existing lexical resources as an additional inductive bias.


It is worthwhile to note that not all words have connotative meanings that are distinct from denotational meanings, and in some cases, it can be difficult to determine whether the overall sentiment is drawn from denotational or connotative meanings exclusively, or both.

we encompass any sentiment from either type of meanings into the lexicon, where non-neutral polarity prevails over neutral one if some meanings lead to neutral while others to non-neutral.


With the goal of obtaining a broad-coverage lexicon in mind, we find that relying only on the structure of semantic prosody is limiting, due to relatively small sets of connotative predicates available.

6 we extend the graph structure as an overlay of two sub-graphs ( Figure 1) as described below: This sub-graph is the bipartite graph that encodes the selectional preference of connotative predicates over their arguments.


Second, for some words with distinct sentiment or strong connotation, it can be difficult or even unnatural to draw a precise distinction between connotation and sentiment, e.g., "efficient".

sentiment lexicons can serve as a surrogate to measure a subset of connotation words induced by the algorithms, as shown in Table 3 with respect to General Inquirer (Stone and Hunt (1963)) and MPQA (Wilson et al.


However, because the hard constraints in §2.3 are defined based on the assumption that all the variables are binary integers, those constraints are not as meaningful when considered for real numbers.

we revise those hard constraints to encode various semantic relations (WordNet and semantic coordination) more directly.


It depends on what is the natural degree of agreement over subtle connotation among people.

we also report the degree of agreement among human judges in Table 7, where we compute the agreement of one Turker with respect to the gold standard drawn from the rest of the Turkers, and take the average across over all five Turkers 18 .


Where the HMM jump model considers only the prior state, fertility requires looking across the whole state space.

the standard forward-backward and Viterbi algorithms do not apply.


Here we are interested in the problem of finding the most likely alignment of a sentence pair e, f .

we need to solve the combinatorial optimization problem arg max a Pr(f , a|e).


Note how the langrangian adds one additional term word, scaled by a value indicating whether that word is aligned in the current position.

because it is only added for those words that are aligned, we can merge this with the log p f j e a j terms in both f and g. we can solve arg max a f (a) + i,j u (k−1) (i, j)y a (i, j) using the standard Viterbi algorithm.


However, once monsters are encountered, they quickly start dominating, with no chance for PRO to recover since it accumulates n-best lists, and thus also monsters, over iterations.

pRO keeps jumping up and down and converges to random values, as Figure 1 shows.


Yet, the results are not as good as when using a max cut-off for the length.

we recommend using the latter as a default setting.


We can discard all features whose weight is zero from the trained model 1 without any loss.

l 1 -regularizers have the ability to easily and automatically yield compact models without strong concern over feature selection.


Hereafter, we strictly assume that L(w; D) and Ω(w) are both convex in w. Then, the properties of our method are unaffected by the selection of L(w; D) and Ω(w).

we ignore their specific definition in this section.


The intuition of this template is that the distribution of the feature weights in trained model often takes a form a similar to that of the 'power law' in the case of the large feature sets.

using an exponential function with a scale and bias seems to be appropriate for fitting them.


In this example, two continuous topic chains or links (via linked priors) exist for the time interval [t-1, t+1]: one in light grey color, and the other in black.

as shown, there may be more than one topic chain/link (5-20 in our experiments) for a certain time interval 1 .we sort multiple sentiment series according to their accumulative weights of topics over each link: ∑ .


Moreover, determiner-noun phrases are, in superficial syntactic terms, similar to the adjective-noun phrases that have already been extensively studied from a cDSM perspective by Baroni and Zampar-elli (2010), Guevara (2010) and Mitchell and Lapata (2010).

we can straightforwardly extend the methods already proposed for adjective-noun phrases to DPs.


Considering the sort of semantic space we used (which we took to be a reasonable starting point because of its effectiveness in a standard lexical task), it is actually surprising that we obtained the significant results we obtained.

a top priority in future work is to explore different contextual features, such as adverbs and grammatical terms, that might carry information that is more directly relevant to the semantics of determiners.


We analyzed a tweet dataset which includes 326,747 posts (Details are given in Section 3) collected during 2011 London Riots, and result reveals that at least 18.91% of these tweets bear uncertainty characteristics 1 .

distinguishing uncertain statements from factual ones is crucial for users to synthesize social media information to produce or derive reliable interpretations, and this is expected helpful for applications like credibility analysis (Castillo et al., 2011) and rumor detection (Qazvinian et al., 2011) based on social media.


Previous research shows that uncertainty identification is domain dependent as the usage of hedge cues varies widely in different domains (Morante and Sporleder, 2012).

the employment of existing out-of-domain corpus to social media context is ineffective.


Annotation result is displayed in  as Dynamic and all of them are buletic modality 4 which shares similarity with Doxastic.

we consider Dynamic together with Domestic in the error analysis for simplicity.


During the preliminary annotation, we found that uncertainty cue-phrase is a good indicator for uncertainty tweets since tweets labeled as uncertain always contain at least one cue-phrase.

annotators are also required identify cue-phrases which trigger the sense of uncertainty in the tweet.


Our preliminary experiments showed that combining unigrams with bigrams and trigrams gave better performance than using any one or two of these three features.

we just report the result based on the combination of them as n-gram features.


Roth and Frank selected document pairs based on clustering, meaning that the pairs had high lexical overlap, often resulting in minimal rewrites of each other.

despite ignoring all context, their baseline method (lemmaalignment) worked quite well.


In our scenario, 'hat' is stronger than 'glasses': the referents wearing a hat are a proper subset of those wearing glasses.

given the players' goal, if the speaker says 'glasses', the listener should draw the scalar implicature that 'hat' is false.


Thus, given the players' goal, if the speaker says 'glasses', the listener should draw the scalar implicature that 'hat' is false.

'glasses' comes to unambiguously refer to r 2 (Fig.


However, a greedy algorithm does not always lead to satisfactory results, as our experiment shows in Section 4.2.

we propose to use the genetic algorithm (Holland, 1992) as the search strategy, which can lead to better results.


4 Comparing the two Stanford parsers in Table 2, the factored model provides clear improvements 3 We use the standard data split suggested by the PCTB 6 file manifest.

our results differ from those previously reported on other splits.


By running a complete inference process, joint model can be applied to re-parsing all candidates explored by a parser.

step3 can be viewed as full-scale candidates reranking because the reranking scope is beyond the limited n-best output currently cached in H k .


Let to be LEFT(i)/RIGHT(i) actions where 1 .

the set of all possible one-action extension of is: Here, ' ' means insert to the end of .


For example, the two sentences in Figure 1 intuitively share the same argument structures, but this overlap can only be identified if the prepositional phrase, "at 5 o'clock," is treated as a modifier.

representing the argument/modifier distinction can help the learner find useful argument structures which generalize robustly.


Importantly, this makes the learning problem for our model less sparse than for TSGs; our model can derive the trees in a corpus using fewer types of elementary trees than a TSG.

the distribution over these elementary trees is easier to estimate.


For this study, we want to use data that is rich in opinions and implicatures.

we used the corpus from (Conrad et al., 2012), which consists of 134 documents from blogs and editorials about a controversial topic, "the Affordable Care Act".


Also, the two may be combined together in chains.

we measure agreement for gfbf and influencer spans together, treating them as one type.


Recall that the annotator could choose whether (s)he is certain about the annotation.

we evaluate two sets: all annotations and only those annotations that both annotators are certain about.


Note that the agent span is in fact a noun phrase (even though it refers to an event).

it doesn't break the rule that all agent gfbf spans should be noun phrases.


Because the algorithm optimizes for the total sum of all SCUs, when there is no threshold similarity to count as a match, it favors matching shorter substrings to SCUs with higher weights.

we add a threshold to the algorithm, below which matches are not considered.


LVc, min, 0.40 did not rank as highly for Speaman and Kendall's tau correlations, but the Student's T result in column 3 of Table 1 shows that this is the only variant in the table that yields absolute scores that are not significantly different from the human annotated scores.

this variant best balances criteria 1 and 2.


Unigram QA Model The QA system uses up to trigram features (Table 1 shows examples of unigram and bigram features).

it is able to learn, for instance, that a POS sequence of IN CD NNS is likely an answer to a when question (such as: in 5 years).


Then we would have missed this feature if we only used top unigram features.

we re-train the model with only unigram features to make sure weights are "assigned properly" among only unigram features.


The two descending lines at the bottom reflect the fact that the majority-voting mechanism from the QA system was too simple: F 1 drops as K increases.

we also computed F 1 's assuming perfect voting: a voting oracle that always selects the correct answer as long as the QA system produces one, thus the two ascending lines in the center of Figure 2.


Manual segmentation is accurate but labor-intensive and costly.

automatic story segmentation approaches are highly demanded.


Recent studies have shown that intrinsic dimensionality of natural text corpus is significantly lower than its ambient Euclidean space (Belkin and Niyogi, 2002;Xie et al., 2012).

laplacian Eigenmaps (lE) was proposed to compute corresponding natural low-dimensional structure.


The native speakers of Indian languages prefer to speak what is written.

there are fewer variations in word-phone mapping as compared to US English.


The rest of this section describes how we set the right window size w. Though each entity may have its own optimal w, we find optimizing for each entity may negatively influence on considering relationships with entities of different window sizes.

we instead find the optimal window sizeŵ to maximize the global "symmetry" of the given two graphs.


The different results of RNNLM and FLM show that they provide different estimates of the next word.

a combination of them may reduce the perplexities of table 2.


J is the vocabulary size in the target domain.

the data set in the source domain is represented by an We factorize these matrices using nonnegative matrix factorization (Lee and Seung, 2000) to find topics as follows:  2000] to factorize the original matrices.


When each document in the source domain has a class label yn, we can train a classifier on the training data set {sn, yn} N n=1.

the class label of the mapped document in the target domain s(tm) is assigned by the classifier.


Indeed, most of the X, Y, and Z triples, when used in the context of this joke, will not be considered funny.

the main challenge in this work is to "fill in" the slots in the joke template in a way that the whole phrase is considered funny.


In the n-gram model, for example, these two attributes are jointly taken into account in the ordered word-sequence.

the n-gram model can only be effectively implemented within a short history-context (e.g.


ሺ݄ ൌ ݓ ି ሻ ؠ ሺݓ ି א ݄ሻ ת ሺ∆ൌ ݇ሻ.

the probability in Eq.1 can be written as: where the likelihood ܲሺݓ ି א ݄, ∆ൌ ݐ|݇ ൌ ݓ ሻ measures how likely the joint event ሺݓ ି א ݄, ∆ൌ ݇ሻ would be observed given the target-word ݓ .


In addition, words may be deleted and inserted in the corrected sentence, which makes the alignment difficult.

we detect word deletion, insertion, and replacement by dynamic programming 4 .


These error-correction pairs are a mixture of grammatical mistakes, spelling errors, and semantic confusions.

we identify pairs due to semantic confusion; we exclude grammatical error corrections by eliminating pairs whose error and correction have different part-of-speech (POS) 5 , and exclude spelling error corrections based on edit-distance.


The lower κ on generative models may be because the distractors are semantically too close to the target (correct answer) as following examples: The coalition has *published/issued a report saying that ... .

the quiz from generative models is not reliable since both published and issued are correct.


In this paper, we focus on the task of identifying light verb constructions (LVCs) in English and Hungarian free texts.

the same task will be carried out for English and a morphologically rich language.


they were included in the base feature set) since it was also effective in the case of the other language.

a multilingual approach may be also beneficial in the case of monolingual applications as well.


Recognizing each BSRs in pairs exactly is difficult for computers.

we should generate specialized data sets consisting of t1-t2 pairs decomposed into BSRs and a methodology for generating such data sets since such data and methodologies for Japanese are unavailable at present.


BSRs that yielded scores of less than 70% occurred less than 3 times, and those that yielded scores of not more than 70% occurred 3 times or more, except for Temporal and Transparent head.

the frequencies of BSRs are related to F 1 scores, and we should consider how to build systems that recognize infrequent BSRs accurately.


Comparable corpora are defined as pairs of monolingual corpora selected according to the criteria of content similarity but non-direct translation in different languages, which reduces limitation of matching source language and target language documents.

comparable corpora have the advantage over parallel corpora in which they are more up-to-date, abundant and accessible (Ji, 2009).


It gives high weights to the topic which appears frequently in a specific document and rarely appears in other documents.

then the relation between and then taking the logarithm, which is calculated as follows: the tFIDF is calculated as follows: the tFIDF score of the topic k over document  the similarity between S m  and t m  is given by: the similarity between  Based on the Bayesian formula, the probability that a given topic  is assigned to a particular target language document t m  is expressed: the sum of all probabilities that all topics  are assigned to a particular document t m  is a constant  , thus equation 8is converted as follows: (9) According to the total probability formula, the similarity between S m  and t m  is given by:  Braschler et al.


M x quit,John , contains a count of the number of times the term j instantiated the template slot i in the corpus.

each row M i, * corresponds to an argument vector for slot i.


The agreement AG(p, p') balances the two factors by taking the product.

relations that have high level of agreement will have more subjects in common, and higher proportion of shared subjects with shared objects.


As it is shown in Figure 2, Linked Data relations are often heterogeneous.

finding equivalent relations to improve coverage is important.


For instance, swap is quite common for the Hindi-English pair because postpositions depend on nouns in Hindi whereas nouns depend on prepositions in English.

the enhancement for the swapped alignment alone results in a large error reduction, as in Table 2.


These translations are also similar in context.

this pruning method deletes irrelevant translations form the src-trg phrase table.


In the last significance test we set the combined phrase table system without pruning as our base system and we compare the result of the pseudo system with it, the significance level is 99%.

we can conclude the proposed method obtains the best results and its difference with pseudo parallel corpus method is not significant.


First, all Twitter data were collected by keyword-based crawling.

we can expect a topical similarity between development, test and training data.


The low-quality bilingual data degrades the quality of word alignment and leads to the incorrect phrase pairs, which will hurt the translation performance of phrase-based SMT systems (Koehn et al., 2003;Och and Ney, 2004).

it is very important to exploit data quality information to improve the translation modeling.


However, simply filtering out this kind of sentence pairs may lead to some loss of native English expressions, thereby the trans-lation performance is unstable since both nonparallel sentence pairs and non-literal but parallel sentence pairs are filtered.

we use the importance score of each sentence pair to estimate the phrase translation probabilities.


The reason is two-fold: on the one hand, the non-literal parallel data makes false positive in noisy data detection; on the other hand, large-scale SMT systems is relatively robust and tolerant to noisy data, especially when we remove frequency-1 phrase pairs.

we propose to integrate the importance scores when re-estimating phrase pair probabilities in this paper.


However, estimating the probabilities of rules extracted from hypergraphs is an NP-complete problem, which is computationally infeasible.

we propose a divide-and-conquer strategy by decomposing a hypergraph into a set of independent subhypergraphs.


However, since a hypergraph is constructed from an nbest list, it can only represent partial space of all alignments (p(A|H) < 1) because of the limiting size of hyperedges learned from the list.

we need to enumerate all possible alignments in a hypergraph to obtain the probability sum p(A|H).


In this work, similarity is measured using the cosine distance similarity metric, given by where d i corresponds to a vector indicating affix distribution, and "•" denotes the inner product of the two vectors.

for a specific span to be translated, we first analyze it to get the corresponding stem sequence and related affix distribution represented as a vector.


If the source part is matched, the similarity will be calculated for each candidate translation rule by cosine similarity (as in equation 3).

in addition to the traditional translation features on stem level, our model also adds the affix similarity score as a dynamic feature into the log-linear model (Och and Ney, 2002).


The Antithesis relation controls U 1 from 0 to 9 and U 2 from 10 to 21.


Obviously, lexical cohesion devices are frequently used in real-world texts.

capturing lexical cohesion devices is very useful for document-level machine translation.


2010) combined the benefits of the above two strategies: their approaches reorder an input sentence based on a set of reordering rules defined over the source sentence's syntax parse tree.

the re-ordered source sentence resembles the word order of its target translation.


Although it is possible to have multiple lexicalizations in a partially lexicalized rule (which will exponentially increase the total number of rules), we observe that most of the time reordering is triggered by a single constituent.

we keep one lexicalization in the partially lexicalized rules.


As multiple levels of rules can lead to the same permutation with different probabilities, we take the weighted sum of probabilities from all matching rules (with the same rhs).

the permutation decision is not based on any particular rule, but the combination of all the rules matching different levels of context.


In MBRAR, we replace the loss function with the gain function that measure the correlation between answer candidates.

the objective of the MBRAR approach for single QA system is to find the answer candidate that is most supported by other candidates under QA system's distribution, which can be formally written as: denotes the hypothesis distribution estimated on the search space H(Q) based on the following log-linear formulation: is the posterior probability of the answer candidate A k based on QA system's ranking model, β is a scaling factor which controls the distribution P (•) sharp (when β > 1) or smooth (when β < 1).


One of the primary reasons is that information seekers cannot retrieve answers they need and thus post another new question consequently.

it becomes more and more important to find semantically similar questions.


When the size of data is small, LSTI tends to just align the common words and thus cannot find the corresponding relations among the focus words in CQA triples.

more CQA triples may result in better performance for our method.


A predicate has only 2 arguments in this representation.

a di-transitive verb is in terms of two relations.


Training by SVM is performed to find the optimal hyperplane consisting of SVs, and only the SVs affect the performance.

if some training document reduces the overall performance of text classification because of an outlier, we can assume that the document is a SV.


Furthermore, some scientists believe that QM is the final principle of physics even the whole natural science.

more and more researchers have expanded the study of QM in other fields of science, and it has affected almost every aspect of natural science and technology deeply, such as quantum computation.


As we mentioned in the introduction, the major principle of quantum classifier (QC) is to consider the classifier as a physical system and the whole process of classification as the evolutionary process of a closed quantum system.

the evolution of the quantum system can be described by a unitary operator (unitary matrix), and the remaining job is to find the correct or optimal unitary operator.


Since H is a Hermitian matrix, we only need to determine ( + ) free real parameters, provided that the dimension of H is (m+w).

the problem of determining H can be regarded as a classical optimization problem, which can be resolved by various optimization algorithms (Chen and Kudlek, 2001).


Actually, some single samples could lead to an accuracy of more than 90%, while some others may produce an accuracy lower than 30%.

the most significant factor for QC is the quality of the training samples rather than the quantity.


We might, for example, consider dramatic (term X) and pleasant (term Y) to correlate with a review of a good movie (task A), while finding them of opposite polarity in the context of a dating profile (task B).

good vectors for X and Y should yield an inner product close to 1 in the context of task A, and −1 in the context of task B.


The recommendation agent can use both the set of businesses B and the history of question and answers H from the user to select the next query.

formally a recommendation agent is a function π : B × H → Att.


While English is still the top language in Twitter, it is no longer the majority.

the applicability of these approaches is limited.


Unfortunately, Burger's corpus does not include Russian and Spanish data on the same scale as English.

for other languages we construct a new Twitter corpus by downloading tweets from followers of regionspecific news and media feeds.


The set of parameters θ is optimized using a grid search on the development data using F-measure for subjectivity classification.

for English θ = [0.7, 5, 50] meaning that on each iteration the top 50 new terms with a frequency ≥ 5 and probability ≥ 0.7 are added to the lexicon.


is also used for training, "like" will be learned correctly, due to the removal of negation in sample reversion.

the procedure of dual training can correct some learning errors caused by polarity shift.


This further increases the annotation costs because different datasets are needed for different tasks.

strategies to reduce the demand for annotated data are needed.


(2012) shows that using a statistical machine translation system to assess the difficulty of translating an Arabic OCR hypothesis into English gives valuable feedback on OCR quality.

combining additional information with the LMs could reduce recognition errors.


Discriminative reranking allows each hypothesis to be represented as an arbitrary set of features without the need to explicitly model their interactions.

the system benefits from global and potentially complex features which are not available to the baseline OCR decoder.


It is very common that the themes of a corpus evolve over time, and topics of adjacent epochs usually exhibit strong correlations.

it is important to model topics across different documents and over different time periods to detect how the events evolve.


Furthermore, the manually annotated treebanks that these parsers rely on are highly expensive to create.

developing dependency parsing algorithms that can be easily ported from one domain to another-say, from a resource-rich domain to a resource-poor domain-is of great importance.


Similarly, in Step 3 the subtree-based features can be added to the labeled data from the target domain only or from the source and target domains.


Treebank annotation transformation relies on the parallel training data.

the accuracy of source parser decides the accuracy of annotation transformer.


For TAG, this problem is only more severe due to its mild context-sensitivity and even richer combinatorial nature.


We modified the original dependency tree and treat the comma ',' as a conjunction.

',' will have 'Ram' and 'Shyam' as children and 'and' will have ',' and 'Sita' as children.


So, we need to know exactly what the generative model is contributing to the accuracy of a generative-discriminative model combination.

we conduct experiments in sets: in some cases the generative model score is used, and in others it is not used.


We can easily get an F1 score greater than 96%, and an oracle (upper bound) F1 score greater than 99% for the word lattice (Jiang et al., 2008).

a word lattice provides us a good enough search space to allow sufficient interaction among word segmentation, POS tagging and parsing systems.


Notice that, crucially, a pop operation does not change the underlying list at all, and a push operation only adds to the front of a list.

the stack operations are non-destructive, in the sense that once you hold a reference to a stack, the view of the stack through this reference does not change regardless of future operations that are applied to the stack.


However, some of the post-collision adakitic rocks have unusually high K 2 O contents, yielding abnormally high total alkaline contents and K 2 O/ Na 2 O ratios.

these samples significantly depart from the overall trends (Fig.


Analogical inference differs drastically from those approaches, since it finds relations in the source material and solves target equations independently.


However, existing estimation methods require either large amounts of RAM (Stolcke, 2002) or machines (Brants et al., 2007).

practitioners have chosen to use less data (Callison-Burch et al., 2012) or simpler smoothing methods (Brants et al., 2007).


3 Sums and counts are done with exact integer arithmetic.

every floating-point value generated by our toolkit is the result of O(N ) floating-point operations.


Input, the algorithm, block sorting, and output are all threads on a chain of producerconsumer queues.

computation and disk operations happen simultaneously.


A significant novelty of our adaptation technique is its incremental nature; we continuously update the topic distribution on the evolving test conversation as new utterances become available.

our approach is well-suited to the causal constraint of spoken conversations.


At run-time, however, we construct the conversation history for the tuning and test sets incrementally, one utterance at a time, mirroring a real-world scenario where our knowledge is limited to the utterances that have been spoken up to that point in time.

each development/test utterance is associated with a different conversation history d * , for which we infer a topic distribution θ d * = p(z k |d * ) using the trained LDA model.


The JSD is a smoothed and symmetric version of Kullback-Leibler divergence, which is typically used to compare two probability distributions.

we define the similarity score as sim(θ we obtain a vector of similarity scores indexed by the training conversations.


The resulting number of "true" targets is 1,083, which amounts to 4% of all verbs.

the dataset is highly skewed to correct usages, though this setting expresses well the reality of ESL writing, as shown in (Chodorow et al., 2012).


It follows the intuition that derivationally related words (feed -feeder, blocked -blockage) are, as a rule, semantically highly similar.

knowledge about derivationally related words can be used as a "back off" for sparse vectors in syntactic spaces.


This time, the avgSim (average similarity) smoothing scheme performs best, with the prototype-based scheme in second place.

the results for synonym choice are less clear-cut: derivational smoothing can trade accuracy against  What is more, the BOW "baseline" significantly outperforms all syntactic models, smoothed and unsmoothed, with an almost perfect coverage combined with a higher accuracy.


Such phenomenon typically occurs when the complexity of gold units is high enough to induce low agreement in workers' answers.

the requester is constrained to review gold units and to eventually forgive workers who missed them.


After few pilot annotations with the three types of FE definitions, we noticed that the simplified one achieved a better accuracy and a lower number of untrusted annotators compared to the others.

we use the simplified definitions in both the 2-step and the 1-step approach (Section 3.4).


First, including a word fertility model, IBM 3 and 4 limit (typically to the half of the source sentence length) the number of target words that can be aligned with the null word.

when such limit is reached, these models tend to force low probability, hence less reliable, word alignments.


Although, we have observed improvement in F 1 score over the monolingual case, the gains do not reach significance according to McNemar's test (Dietterich, 1998).

we propose to further refine the quality of word alignment links as follows: Let x be a word in language Σ and y be a word in language Ω and let there exists an alignment link between x and y.


However, words in the original caption of the query image (and its compressed caption) do not overlap exactly with words in the retrieved captions, even when they are semantically very close, which makes it hard to see improvements even when the captions of the new corpus are more general and transferable over other images.

we also report scores based on semantic matching, which gives partial credits to word pairs based on their lexical similarity.


In the conormal product, an edge is introduced iff the corresponding edge exists in at least one input graph.

a conormal product may compensate for omitted temporal relations in the input graphs.


The rationale behind the n-gram module is based on the understanding that terms appearing in non-named entity contexts are likely to be nonreferential, and terms that can be non-referential are ambiguous.

detecting terms that have non-referential usages can also be used to detect ambiguity.


Let p i = P (for|y i ) be the "benefit" of setting x i to 1, where P (for|y i ) is provided by the CRF.

after optimization, y i 's stance is for if its x i is set to 1.


To accomplish this, they solve the "densest subgraph problem" on specifically created "bipartite" graphs centered at "hops" by transforming the problem into an equivalent "minimum set cover" framework.

these boldfaced words often occur as hop-based school-ofthought words.


Recurring illnesses falsely appear to have an unpredictable order.

there can be no universal logical constraints on the order of cancer events.


Textual Features: The textual features extract from 1,500 most frequent words in the training dataset after standard preprocessing steps, namely word segmentation, stopwords removal and stemming 1 .

each answer is represented as a vector containing 1,500 distinct terms weighted by binary scheme.


As shown in Table 2, each question on average receives more than 4 answers on ZHIDAO and more than 10 on YAHOO.

it is possible that there are several answers with high quality to the same question.


The features described so far, however, cannot differentiate between a possible aspect mention which is a target of a subjective expression and one which is not.

the features of the inter-span template are actually built by taking the cross-product of the three described characteristics with all single-span features.


Further, the function o calculates the overlap in terms of tokens of two spans and the function p returns the number of tokens in t that are not contained in g, i. e., those which are outside the overlap (both functions taking into account the class of the span).

the first part of the objective function represents the fraction of correctly proposed contiguous tokens, while the second part penalizes a span for containing too many tokens that are outside the best span.


The results clearly suggest that the impact of sub-jective terms on target terms is higher than the other way round.

if a pipeline architecture is chosen, this order is to be preferred.


However, the results with perfect knowledge of the counterpart entity show (in both directions) that the entities influence each other positively.

the challenge of extracting subjective expressions and their targets is a great candidate for applying supervised, joint inference.


According to (Ku et al., 2006) (Chen et al., 2012) (Fan et al., 2011), SWI can be applied to many fields, such as determining critics opinions about a given product, tweeter classification, summarization of reviews, and message filtering, etc.


Any missing key word in the set of seed words could lead to poor performance.

the seed word set of such algorithms demands high completeness (by containing common polarity words as many as possible).


There are several ways to define the word importance, and we choose normalized TF-IDF (Jones, 1972).

we have f ij ∝ T F −IDF (d i , c j ), and ∥ ⃗ f i ∥ = 1.


It accounts for hardly 1-2% of any given corpus.

it becomes hard to find sufficient number of examples of thwarting to train a classifier.


The basic objective for creating a thwarting detection system was to include such a module in the general sentiment analysis framework.

using document polarity as a feature contradicts the objective of sentiment analysis, which is to find the document polarity.


Most noun phrases are marked with a limited set of case suffixes, and clauses generally end in a verb, inflected with suffixes ending in a limited set of sounds ([a,ʌ,i,jo]).

the phrase-final distribution may not reflect the overall word-final distribution-problematic for some phonotactic approaches.


As already discussed, one particular expert may be better at predicting the kth substructure while some other expert may be more accurate at predicting another substructure.

it is desirable to combine the substructure predictions of all experts to derive a more accurate prediction.


The base scoring functions used in our algorithm have the form In particular, these can be derived from the path experts in H by letting h k t (x, y) = 1 h k t (x)=y k .

the score assigned to y by the base scoring function h t is the number of positions at which y matches the prediction of path expert h t given input x. H Boost is defined as follows in terms of h or h t s: We remark that the analysis and algorithm presented in this section are also applicable with a scoring function that is the product of the scores at each substructure k as opposed to a sum, that is, This can be used for example in the case where the experts are derived from probabilistic models.


Each example in this data set is represented by a sequence of length l ∈ [2,15].

for training we padded the true labels and the expert predictions to normalize the sequence lengths.


In this paper, we consider a simple transformation function, linear projection.

we name the approach DPLP: Discourse Parsing from Linear Projection.


Note that by settingw m = w m A, the decision scoring function can be rewritten asw m v, which is linear in the original surface features.

the expressiveness of DPLP is identical to a linear separator in the original feature space.


Note that for a given i, the matrix i is of (at most) rank-1.

the solution of A can be viewed as the linear combination of a sequence of rank-1 matrices, where each rank-1 matrix is defined by distributional representation v i and the weight difference between the weight of true label w y i and the "expected" weight m η i,m w m .


Here we follow the arc factored method and define the score of a dependency tree as the sum of the scores of all the arcs in the tree.

the optimal dependency tree for T is a spanning tree with the highest score and obtained through the function DT(T,w): ) denotes the score of the arc <e i , r, e j > which is calculated according to its feature representation f(e i ,r,e j ) and a weight vector w. Next, two basic problems need to be solved: how to find the dependency tree with the highest score for T given all the arc scores (i.e.


The digraph keeps only one arc <e i , r, e j > between two nodes which satisfies ( .

we can proceed with a reduction from labeled parsing to unlabeled parsing.


In fact, the score of each arc is calculated as a linear combination of feature weights.

we need to determine the features for arc representation first.


In fact, we observe that some non-projective dependencies produced by the MST algorithm are even reasonable than what they are in the dependency treebank.

it is important to build a manually labeled discourse dependency treebank, which will be our future work.


It is very similar to early updates, but differs in one crucial respect: When an early update is made, search is continued rather than aborted.

the learning algorithm always reaches the end of a document, avoiding the problem that early updates discard parts of the training data.


At this point, crawling has no more links to follow.

the MRS crawling operations 'paint' a subset of the MRS graph as in-scope for a given negation cue.


Unlike the rather complex top-performing systems from the original 2012 competition, our MRS Crawler is defined by a small set of general rules that operate over general-purpose, explicit meaning representations.

our approach scores high on transparency, adaptability, and replicability.


", no matter how we store the data of students and how many records of books are there in our database.

our first step is to fix a notation which abstracts the calculation process of DCS trees, so as to clarify its meaning without the aid of any existing database.


Thus, the semantic distance is bidirectional: the distance between p t and the transformation of p s , and that between p s and the transformation of p t .

the overall semantic error becomes: Finally, we calculate their Euclidean distance: E sem (t|s, θ) can be calculated in exactly the same way.


In fact, the phrases having the same meaning are translation equivalents in different languages, but are paraphrases in one language.

our model can be easily adapted to learn semantic phrase embeddings using paraphrases.


Deep models such as DNN have the potential to be much more representationally efficient for feature learning than shallow models like GMM.

instead of GMM, we use DNN (DBN, DAE and HCDAE) to learn new non-parametric features, which has the similar evolution in speech recognition Hinton et al., 2012).


K = k 1 ((1 − b) + J/avg(l)), and J is the phrase length (l e or l f ), avg(l) is the average phrase length.

we have the second type of input features We adapt and extend bidirectional phrase generative probabilities as the input features, which have been used for domain adaptation (Foster et al., 2010).


In this case, people understand the meaning because of the IT topical context which goes beyond sentence-level analysis and requires more relevant knowledge.

it is important to leverage topic information to learn smarter translation models and achieve better translation performance.


This makes previous approaches inefficient when applied them in real-world commercial SMT systems.

we need to devise a systematical approach to enriching the sentence and inferring its topic more accurately.


This dense representation should preserve the information from the bag-of-words input, meanwhile alleviate data sparse problem.

we use a specially designed mechanism called auto-encoder to solve this problem.


Since the vectors from DAE are trained using information from monolingual training data independently, these vectors may be in-adequate to measure bilingual topic similarity due to their different topic spaces.

in this stage, parallel sentence pairs are used to help connecting the vectors from different languages because they express the same topic.


We then take two vectors as the input to calculate their similarity.

the whole neural network can be fine-tuned towards the supervised criteria with the help of parallel data.


By measuring the similarity between the source texts and bilingual translation rules, the SMT decoder is able to encourage topic relevant translation candidates and penalize topic irrelevant candidates.

it helps to train a smarter translation model with the embedded topic information.


Limited training data prevents the model from getting close to the global optimum.

the model is likely to fall in local optima and lead to unacceptable representations.


In conpproach directly learns the topic repwith an abundancy of related docudditional to the original document from sentence is extracted, the IR method es other relevant documents which proementary topic information.

epresentations learned are more finethus more accurate.


Since the language and translation models operate at the word level, the objective function entails that we let the models learn based on their fractional contribution of the words from the language and translation models.

we estimate the proportional contribution of each word from the language and translation models too, in the E-step.


Furthermore, note the superior performance of unigrams trained on TAC data vs TAC+ff+time -which is similar to our unigrams but trained on a larger but non-TAC dataset that included metadata.

tAC pairs are a useful data source even for non-custom features.


For instance, 85.3% of all Twitter users post less than one update per day as reported at http://www.sysomos.com/insidetwitter/.

their communications are scare even if we could get all of them without rate limiting from Twitter API.


We show that three of six social circles -friend, retweet and user-mention yield better accuracy compared to the user model for all graphs when t ≥ 250.

for effectively classifying a given user v i it is better to take 200 tweets each from 10 neighbors rather than 2,000 tweets from the user.


The amount of time needed can be evaluated for different accuracy levels e.g., 0.75 and 0.95.

with 75% accuracy we classify: • 100 (∼20%) Republican users in 3.6 hours and Democratic users in 2.2 hours for G cand ; • 100 (∼56%) R users in 20 weeks and 100 (∼52%) D users in 8.9 weeks for G ZLR which is 800 times longer that for G cand ; • 100 (∼75%) R users in 12 weeks and 80 (∼60%) D users in 19 weeks for G geo .


For this choice of q, the probability of accepting the new tree (α in Figure 1) is identically one.


We cannot necessarily assume that s(x, y) is greater than s(x, y ) without additional encouragement.

we can complement the constraints in Equation 4 with additional pairwise constraints (Wick et al., 2011): where similarly to Equation 4, the difference in scores scales with the differences in errors with respect to the target y (i) .


instructions in lockstep, differing only in their input data.

sparsely skipping rules and symbols will not save any work.


Because all rules are applied to all parse items, all threads are executing the same sequence of instructions.


Spreading symbols across clusters may be inefficient: if a parse item licenses a given symbol, we will have to enqueue that item to any queue that has the symbol in its signature, no matter how many other symbols are in that cluster.

it makes sense to choose a clustering algorithm that exploits the structure introduced by the pruning masks.


Because the grammar used in the coarse pass is a projection of the grammar used in the fine pass, these coarse scores correlate reasonably closely with the probabilities computed in the fine pass: If a span has a very high or very low score in the coarse pass, it typically has a similar score in the fine pass.

we can use the coarse pass's inside and outside scores as the scaling values for the fine pass's scores.


However, this may not be a true violation of the gold-standard (Huang et al., 2012).

we use a relaxed version of early update, in which all goldstandard actions must fall outside the beam before an update is performed.


However, negative features-features that are not observed in any tree-are still powerful indicators of (un)grammaticality: if we have never seen a PRN that starts with "has," or a span that begins with a quotation mark and ends with a close bracket, then we would like the model to be able to place negative weights on these features.

we use a simple feature hashing scheme where positive features are indexed individually, while nega-  Our goal is to use surface features to replicate the functionality of other annotations, without increasing the state space of our grammar, meaning that the rules rule(r) remain simple, as does the state space used during inference.


Syntax is often driven by heads of constituents, which tend to be located at the beginning or the end, whereas sentiment is more likely to depend on modifiers such as adjectives, which are typically present in the middle of spans.

we augment our existing model with standard sentiment analysis features that look at unigrams and bigrams in the span (Wang and Manning, 2012).


(2013) reveal an interesting cross-lingual property of distributed word representations: there is a strong similarity between the vector spaces across languages that can be easily captured by linear mapping.

vector space models can also be seen as vectors of (latent) semantic concepts, that preserve their "meaning" across languages.


We collect and annotate metaphoric and literal test sentences in four languages.

we compile eight test datasets, four for SVO relations, and four for AN relations.


No English annotators of the test set, and only one Russian annotator out of 6 participated in the selection of the training samples.

we trust that annotator judgments were not biased towards the cases that the system is trained to process.


This results in a search space exponential in the number of slots in the largest template in T , the set of available system templates.

we approximate this computation using beam search.


We show that a learner can use Bayesian model selection to determine the location of function words in their language, even though the input to the model only consists of unsegmented sequences of phones.

our computational models support the hypothesis that function words play a special role in word learning.


As noted earlier, the "function word" model generates function words via adapted nonterminals other than the Word category.

in order to better understand just how the model works, we give the 5 most frequent words in each word category found during 8 MCMC runs of the left-peripheral "function word" grammar above: the present model, initially aimed at segmenting words from continuous speech, shows three interesting characteristics that are also exhibited by human infants: it distinguishes between function words and content words (Shi and Werker, 2001), it allows learners to acquire at least some of the function words of their language (e.g.


Unlike English and other western languages, Chinese do not delimit words by white-space.

word segmentation is a preliminary and important pre-process for Chinese language processing.


As we can see, characters in the first column are all Chinese number characters and characters in the second column and the third column are all Chinese family names and Chinese punctuations respectively.

compared with discrete feature representations, distributed representation can capture the syntactic and semantic similarity between characters.


Polarity-based shifting As will be shown in our experiments, negators can have different shifting power when modifying a positive or a negative phrase.

we explore the use of two different constants for these two situations, i.e., f (s The constant C now can take one of two possible values.


To this end, we make use of the sentiment class information of p 1 , noted as p sen 1 .

the vector of p 2 is calculated as follows: As shown in Equation 6, for the node vector p 1 ∈ R d×1 , we employ a matrix, namely W sen ∈ R d×(d+m) and a tensor, V sen ∈ R (d+m)×(d+m)×d , aiming at explicitly capturing the interplays between the sentiment class of p 1 , denoted as p sen 1 (∈ R m×1 ), and the negator a.


Extracting them can provide essential information for obtaining fine-grained analysis on customers' opinions.

it has attracted a lot of attentions (Hu and Liu, 2004b;Moghaddam and Ester, 2011;Mukherjee and Liu, 2012).


Solid curves and dotted lines respectively mean semantic relations and opinion relations between two candidates.

to resolve these two problems, we present a novel approach with graph co-ranking.


They extracted opinion targets/words in advanced through simple phrase detection.

the extraction performance is far from expectation.


There is no mutual reinforcement between opinion targets and opinion words.

we have where ν has the same role as µ in Eq.1.


To compute P T i k , we first use Kullback-Leibler divergence to measure the semantic distance between any two candidates on the bridge of topics.

we have where , where p(v|z) is the probability of the candidate v to topic z (see Section 3.4).


Intuitively, preference vectors will be different for different candidates.

the co-ranking algorithm would be personalized.


And candidates' confidences will mainly come from the contributions of its preferences.

c t and c o in Eq.4 become: In this section, we explain how to capture semantic relations and opinion relations for constructing transition matrices M tt , M oo and M to .


Then, a symmetric Kullback-Leibler divergence as same as Eq.5 is used to calculate the semantical associations between any two homogenous candidates.

we obtain SA(v t , v t ) and SA(v o , v o ), which correspond to the entries in M tt and M oo , respectively.


In contrast, besides opinion relations, CR and CR WP further take semantic relations into account.

more opinion targets/words can be extracted.


Its extraction is performed separately and neglects the reinforcement between opinion targets and opinion words.

sAs has worse performance than our methods.


We believe the reason is that exploiting word preference can provide more fine information for opinion target/word candidates' confidence estimation.


The equality is not strictly enforced (due to the regularization in the PR objective 2).

all the constraints are applied as soft constraints.


In general, discourse connectives can also be used to connect non-polar (neutral) sentences.

it is hard to directly constrain the posterior expectation for each type of sentiment transitions using inter-sentential discourse connectives.


One reason is that they do not constrain the neutral sentiment.

they could not help disambiguate neutral sentiment from polar sentiment, such as the third example in Table 5.


Nevertheless, a product may have thousands of features, which makes it impractical for a customer to investigate them all.

mining product features automatically from online reviews is shown to be a key step for opinion summarization (Hu and Liu, 2004;Qiu et al., 2009) and fine-grained sentiment analysis (Jiang et al., 2011;Li et al., 2012).


The basic idea is that reviewers tend to comment on product features in similar syntactic structures.

it is natural to mine product features by using syntactic patterns.


However, this pattern becomes too general that it may find many irrelevant cases such as the one in Example 1(c), which is not talking about the product.

it is very difficult for a pattern designer to balance between precision and generalization.


As in Example 1(d), we can see that the word flaws follows mp3 have, but it is not a product feature.

a noise term may be extracted even with high contextual support.


Thus, a noise term may be extracted even with high contextual support.

we shall also verify whether a candidate is really related to the target product.


Word embedding naturally meets the demand above: words that are more semantically similar to each other are located closer in the embedding space (Collobert et al., 2011).

we can use cosine distance between two embedding vectors as the semantic distance measuring metric.


Furthermore, comparing SGW and LEX, both methods are base on random surfer model, but LEX gets better results than SGW.

the wordword semantic similarity relation used in LEX is more reliable than the word-syntactic pattern relation used in SGW.


Although LEX is not so accurate as CONT during seed expansion, its final performance surpasses CONT.

we can draw conclusion that CONT is more suitable for the seed expansion, and LEX is more robust for the final result production.


Also, lengths of many sentences in the review corpora are shorter than 9.

the convolutional approach in CONT is the most effective way among these settings.


As discussed earlier, quality knowledge should be shared by topics across several domains.

it is natural to exploit a frequency-based approach to discover frequent set of terms as quality knowledge.


It means that different aspects may have distinct frequencies.

using a single frequency threshold in the frequency-based approach is not sufficient to extract both generic and specific aspects because the generic aspects will result in numerous spurious aspects (Han et al., 2007).


However, it is difficult to obtain the optimal number of clusters.

we empirically set the number of clusters to the number of domains in our experiments.


It is not tricky to select "Fans", "slump", and "Hawks" as linkable mentions, but other mentions such as "stay up" and "stay positive" are not prominent.

it is challenging to create sufficient high quality labeled tweets for supervised models and worth considering semi-supervised learning with the exploration of unlabeled data.


The context of a single tweet usually cannot provide enough information for prominent mention detection and similarity computing for disambiguation.

a collective inference model over multiple tweets in the semi-supervised setting is desirable.


However, the shortness of a single tweet means that it may not provide enough topical clues.

it is important to extend this evidence to capture semantic relatedness information from multiple tweets.


The goal of the proposed framework is to ensure that the refined labels of unlabeled nodes are consistent with their strongly connected nodes, as well as not too far away from their initial labels.

the above optimization problem can be solved directly since Q(Y) is convex (Zhu et al., 2003;Zhou et al., 2004  a closed form solution can be derived as Y * u = (D uu + µI uu − W uu ) −1 (W ul Y l + µY 0 u ).


A potential obstacle, of course, is that automatic parsers may not be sufficiently representative of human readers, insofar as errors that a parser makes may not be problematic for human comprehension; moreover, parsers are rarely successful in fully recovering the intended interpretation for sentences of moderate length, even with carefully edited news text.

we examine two reranking strategies, one a simple baseline approach and the other using an SVM reranker (Joachims, 2002).


One key feature of our approach is that the grammar extracted from the training data is linguistically principled in that it obeys the extended locality principle of Tree Adjoining Grammars.

the extracted grammar is compact and can be manually modified to fit the need of an application as shown by the good results obtained when using the MAN-EXP configuration.


By handling deletion using a probabilistic model trained on semantic representations, we can avoid deleting obligatory arguments.

in our approach, semantic subformulae which are related to a predicate by a core thematic roles (e.g., agent and patient) are never considered for deletion.


During the construction, all the arcs are built by LEFT-ARC or RIGHT-ARC, which links the top of the stack and the front of the buffer.

we prefer L(σ) to be as orderly as possible, to make the words to be linked sooner on the top of the stack.


On contrast, a deep dependency structure, like GR graph, approximates deep syntactic or semantic information of a sentence as a whole, and therefore is more dense.

permutation system with a very low k is incapable to handle more cases.


Intuitively, if several parsers disagree on an unlabeled sentence, it implies that the unlabeled sentence contains some difficult syntactic phenomena which are not sufficiently covered in manually labeled data.

exploiting such unlabeled data may introduce more discriminative syntactic knowledge, largely compensating labeled training data.


Training with the combined labeled and unlabeled data, the objective is to maximize the mixed likelihood: Since D ′ contains much more instances than D (1.7M vs. 40K for English, and 4M vs. 16K for Chinese), it is likely that the unlabeled data may overwhelm the labeled data during SGD training.

we propose a simple corpus-weighting strategy, as shown in Algorithm 1, where is the subset of training data used in k th update and b is the batch size; η k is the update step, which is adjusted following the simulated annealing procedure (Finkel et al., 2008).